Last updated: 2022-02-26

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Weight QC

#number of imputed weights
nrow(qclist_all)
[1] 6749
#number of imputed weights by chromosome
table(qclist_all$chr)

  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
693 529 449 280 351 381 339 268 267 299 405 423 131 247 240 250 343 112 336 180 
 21  22 
 67 159 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 4358
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.6457

Check convergence of parameters

Version Author Date
0974c69 sq-96 2022-02-14
e6bc169 sq-96 2022-02-13
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior)
     gene       snp 
0.0121777 0.0003704 
#estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var)
 gene   snp 
8.728 8.899 
#report sample size
print(sample_size)
[1] 62892
#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)
[1]    6749 5017190
#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve)
   gene     snp 
0.01141 0.26295 
#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))
[1] 0.06078 1.45686

Genes with highest PIPs

Version Author Date
0974c69 sq-96 2022-02-14
           genename region_tag susie_pip   mu2       PVE      z num_eqtl
5632          CAND2        3_9    0.8555 22.92 0.0003117 -4.854        1
7788        NCKAP5L      12_31    0.8257 27.05 0.0003551  5.000        1
3444          GTF3A       13_7    0.7913 23.01 0.0002896 -4.478        2
11216       CYP21A2       6_26    0.7664 38.72 0.0004718  7.835        1
11883 RP11-209K10.2      15_22    0.7522 27.46 0.0003284 -5.056        1
7394       TP53INP1       8_66    0.7463 25.33 0.0003006 -5.474        1
6171        ARL14EP      11_21    0.7361 22.13 0.0002590 -4.512        3
10272         PARVA       11_9    0.7148 21.99 0.0002500  3.862        1
3551         KBTBD4      11_29    0.7095 26.51 0.0002990 -5.098        1
2050         DNASE2      19_10    0.7071 19.19 0.0002157 -3.744        1
4127         ZNF236      18_45    0.6921 20.89 0.0002298 -4.378        1
8335         CLSTN1        1_7    0.6180 20.20 0.0001985  3.978        1
6831           RPL8       8_94    0.6080 26.54 0.0002566 -5.063        1
1320        CWF19L1      10_64    0.5714 32.84 0.0002984 -5.742        2
9797          SLIT1      10_62    0.5606 23.74 0.0002116  4.762        1
6558          AP3S2      15_41    0.5570 39.32 0.0003483  6.483        1
8968         ALS2CL       3_33    0.5364 22.70 0.0001936  3.405        1
5574          MRPS5       2_57    0.5288 22.15 0.0001862 -3.737        1
11765 RP11-110I1.12      11_71    0.5256 18.71 0.0001563  3.747        1
5773          CRIP3       6_33    0.5254 21.27 0.0001777  4.511        2

Genes with largest effect sizes

Version Author Date
0974c69 sq-96 2022-02-14
           genename region_tag susie_pip   mu2       PVE      z num_eqtl
9887        NCR3LG1      11_12   0.02703 67.63 2.907e-05 -8.447        2
12661     LINC01126       2_27   0.03031 54.49 2.626e-05 -8.377        1
6291          JAZF1       7_23   0.01529 42.70 1.038e-05 -6.628        1
9311         UBE2E2       3_17   0.45738 39.65 2.883e-04  6.058        2
6558          AP3S2      15_41   0.55702 39.32 3.483e-04  6.483        1
6667          UBE2Z      17_28   0.05352 39.28 3.343e-05 -6.797        1
10351      TMEM229B      14_32   0.27272 38.81 1.683e-04 -3.685        2
11216       CYP21A2       6_26   0.76639 38.72 4.718e-04  7.835        1
4550          P2RX4      12_74   0.19404 38.50 1.188e-04  4.087        1
2084          RASA4       7_63   0.14390 33.96 7.771e-05 -4.470        1
10830       SYNJ2BP      14_32   0.15324 33.12 8.070e-05 -3.228        1
1320        CWF19L1      10_64   0.57140 32.84 2.984e-04 -5.742        2
2887          NRBP1       2_16   0.02462 32.74 1.281e-05 -5.595        1
6867          FMNL3      12_31   0.17840 32.21 9.137e-05  3.719        1
6223         GPR180      13_47   0.20330 31.88 1.030e-04 -3.353        1
8847        CCDC121       2_16   0.13639 31.87 6.912e-05  3.505        1
6456           ART3       4_51   0.22417 31.82 1.134e-04 -3.686        2
191           CEP68       2_42   0.50066 31.63 2.518e-04  6.229        2
7489        SDCCAG3       9_73   0.31043 31.39 1.549e-04 -3.739        2
9802  RP11-195F19.5       9_27   0.36489 31.33 1.818e-04 -3.408        2

Genes with highest PVE

           genename region_tag susie_pip   mu2       PVE      z num_eqtl
11216       CYP21A2       6_26    0.7664 38.72 0.0004718  7.835        1
7788        NCKAP5L      12_31    0.8257 27.05 0.0003551  5.000        1
6558          AP3S2      15_41    0.5570 39.32 0.0003483  6.483        1
11883 RP11-209K10.2      15_22    0.7522 27.46 0.0003284 -5.056        1
5632          CAND2        3_9    0.8555 22.92 0.0003117 -4.854        1
7394       TP53INP1       8_66    0.7463 25.33 0.0003006 -5.474        1
3551         KBTBD4      11_29    0.7095 26.51 0.0002990 -5.098        1
1320        CWF19L1      10_64    0.5714 32.84 0.0002984 -5.742        2
3444          GTF3A       13_7    0.7913 23.01 0.0002896 -4.478        2
9311         UBE2E2       3_17    0.4574 39.65 0.0002883  6.058        2
6171        ARL14EP      11_21    0.7361 22.13 0.0002590 -4.512        3
6831           RPL8       8_94    0.6080 26.54 0.0002566 -5.063        1
191           CEP68       2_42    0.5007 31.63 0.0002518  6.229        2
10272         PARVA       11_9    0.7148 21.99 0.0002500  3.862        1
3522        BHLHE41      12_18    0.4800 30.18 0.0002304  5.640        1
4127         ZNF236      18_45    0.6921 20.89 0.0002298 -4.378        1
2050         DNASE2      19_10    0.7071 19.19 0.0002157 -3.744        1
9797          SLIT1      10_62    0.5606 23.74 0.0002116  4.762        1
10501        MAP3K3      17_37    0.4887 26.31 0.0002045 -5.170        1
8335         CLSTN1        1_7    0.6180 20.20 0.0001985  3.978        1

Genes with largest z scores

       genename region_tag susie_pip   mu2       PVE      z num_eqtl
9887    NCR3LG1      11_12   0.02703 67.63 2.907e-05 -8.447        2
12661 LINC01126       2_27   0.03031 54.49 2.626e-05 -8.377        1
11216   CYP21A2       6_26   0.76639 38.72 4.718e-04  7.835        1
6667      UBE2Z      17_28   0.05352 39.28 3.343e-05 -6.797        1
6291      JAZF1       7_23   0.01529 42.70 1.038e-05 -6.628        1
6558      AP3S2      15_41   0.55702 39.32 3.483e-04  6.483        1
191       CEP68       2_42   0.50066 31.63 2.518e-04  6.229        2
9311     UBE2E2       3_17   0.45738 39.65 2.883e-04  6.058        2
10639      MICB       6_25   0.38273 28.87 1.757e-04  5.917        1
1320    CWF19L1      10_64   0.57140 32.84 2.984e-04 -5.742        2
3522    BHLHE41      12_18   0.48002 30.18 2.304e-04  5.640        1
2887      NRBP1       2_16   0.02462 32.74 1.281e-05 -5.595        1
11110       LTA       6_25   0.04071 27.69 1.793e-05  5.500        1
7394   TP53INP1       8_66   0.74634 25.33 3.006e-04 -5.474        1
326    ATP6V0A1      17_25   0.11561 26.56 4.882e-05  5.188        2
10501    MAP3K3      17_37   0.48875 26.31 2.045e-04 -5.170        1
3848     TSPAN8      12_44   0.24678 27.07 1.062e-04  5.137        1
3551     KBTBD4      11_29   0.70951 26.51 2.990e-04 -5.098        1
10594     PSMB8       6_27   0.20581 27.99 9.160e-05  5.081        1
6831       RPL8       8_94   0.60805 26.54 2.566e-04 -5.063        1

Comparing z scores and PIPs

Version Author Date
0974c69 sq-96 2022-02-14

Version Author Date
0974c69 sq-96 2022-02-14
[1] 0.006075
       genename region_tag susie_pip   mu2       PVE      z num_eqtl
9887    NCR3LG1      11_12   0.02703 67.63 2.907e-05 -8.447        2
12661 LINC01126       2_27   0.03031 54.49 2.626e-05 -8.377        1
11216   CYP21A2       6_26   0.76639 38.72 4.718e-04  7.835        1
6667      UBE2Z      17_28   0.05352 39.28 3.343e-05 -6.797        1
6291      JAZF1       7_23   0.01529 42.70 1.038e-05 -6.628        1
6558      AP3S2      15_41   0.55702 39.32 3.483e-04  6.483        1
191       CEP68       2_42   0.50066 31.63 2.518e-04  6.229        2
9311     UBE2E2       3_17   0.45738 39.65 2.883e-04  6.058        2
10639      MICB       6_25   0.38273 28.87 1.757e-04  5.917        1
1320    CWF19L1      10_64   0.57140 32.84 2.984e-04 -5.742        2
3522    BHLHE41      12_18   0.48002 30.18 2.304e-04  5.640        1
2887      NRBP1       2_16   0.02462 32.74 1.281e-05 -5.595        1
11110       LTA       6_25   0.04071 27.69 1.793e-05  5.500        1
7394   TP53INP1       8_66   0.74634 25.33 3.006e-04 -5.474        1
326    ATP6V0A1      17_25   0.11561 26.56 4.882e-05  5.188        2
10501    MAP3K3      17_37   0.48875 26.31 2.045e-04 -5.170        1
3848     TSPAN8      12_44   0.24678 27.07 1.062e-04  5.137        1
3551     KBTBD4      11_29   0.70951 26.51 2.990e-04 -5.098        1
10594     PSMB8       6_27   0.20581 27.99 9.160e-05  5.081        1
6831       RPL8       8_94   0.60805 26.54 2.566e-04 -5.063        1

Gene set enrichment for genes with PIP>0.5

[1] 21
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2021... Done.
  Querying GO_Cellular_Component_2021... Done.
  Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"

Version Author Date
0974c69 sq-96 2022-02-14
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"

Version Author Date
0974c69 sq-96 2022-02-14
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
                                                           Description     FDR
22                           Late onset congenital adrenal hyperplasia 0.01134
32 Hyperandrogenism, Nonclassic Type, due to 21-Hydroxylase Deficiency 0.01134
34     Congenital adrenal hyperplasia due to 21 hydroxylase deficiency 0.01134
38                      SPINOCEREBELLAR ATAXIA, AUTOSOMAL RECESSIVE 17 0.01134
1                                       Congenital adrenal hyperplasia 0.05394
2                                                  Atrial Fibrillation 0.05394
8                            Glomerulonephritis, Membranoproliferative 0.05394
19                                      Paroxysmal atrial fibrillation 0.05394
33                                      Persistent atrial fibrillation 0.05394
35                                        familial atrial fibrillation 0.05394
   Ratio  BgRatio
22  1/11   1/9703
32  1/11   1/9703
34  1/11   1/9703
38  1/11   1/9703
1   1/11   9/9703
2   2/11 160/9703
8   1/11   7/9703
19  2/11 156/9703
33  2/11 156/9703
35  2/11 156/9703
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!

Version Author Date
0974c69 sq-96 2022-02-14
NULL

PIP Manhattan Plot

Version Author Date
0974c69 sq-96 2022-02-14

Sensitivity, specificity and precision for silver standard genes

#number of genes in known annotations
print(length(known_annotations))
[1] 72
#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))
[1] 20
#significance threshold for TWAS
print(sig_thresh)
[1] 4.482
#number of ctwas genes
length(ctwas_genes)
[1] 21
#number of TWAS genes
length(twas_genes)
[1] 41
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
           genename region_tag susie_pip   mu2       PVE      z num_eqtl
8335         CLSTN1        1_7    0.6180 20.20 0.0001985  3.978        1
5574          MRPS5       2_57    0.5288 22.15 0.0001862 -3.737        1
8968         ALS2CL       3_33    0.5364 22.70 0.0001936  3.405        1
10272         PARVA       11_9    0.7148 21.99 0.0002500  3.862        1
11765 RP11-110I1.12      11_71    0.5256 18.71 0.0001563  3.747        1
4127         ZNF236      18_45    0.6921 20.89 0.0002298 -4.378        1
2050         DNASE2      19_10    0.7071 19.19 0.0002157 -3.744        1
3444          GTF3A       13_7    0.7913 23.01 0.0002896 -4.478        2
#sensitivity / recall
print(sensitivity)
ctwas  TWAS 
    0     0 
#specificity
print(specificity)
 ctwas   TWAS 
0.9969 0.9939 
#precision / PPV
print(precision)
ctwas  TWAS 
    0     0 

Version Author Date
0974c69 sq-96 2022-02-14

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.3.1      forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7      
 [5] purrr_0.3.4       readr_2.1.1       tidyr_1.1.4       tidyverse_1.3.1  
 [9] tibble_3.1.6      WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0      
[13] cowplot_1.0.0     ggplot2_3.3.5     workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] fs_1.5.2          lubridate_1.8.0   bit64_4.0.5       doParallel_1.0.17
 [5] httr_1.4.2        rprojroot_2.0.2   tools_3.6.1       backports_1.4.1  
 [9] doRNG_1.8.2       utf8_1.2.2        R6_2.5.1          vipor_0.4.5      
[13] DBI_1.1.2         colorspace_2.0-2  withr_2.4.3       ggrastr_1.0.1    
[17] tidyselect_1.1.1  bit_4.0.4         curl_4.3.2        compiler_3.6.1   
[21] git2r_0.26.1      rvest_1.0.2       cli_3.1.0         Cairo_1.5-12.2   
[25] xml2_1.3.3        labeling_0.4.2    scales_1.1.1      apcluster_1.4.8  
[29] digest_0.6.29     rmarkdown_2.11    svglite_1.2.2     pkgconfig_2.0.3  
[33] htmltools_0.5.2   dbplyr_2.1.1      fastmap_1.1.0     highr_0.9        
[37] rlang_1.0.1       rstudioapi_0.13   RSQLite_2.2.8     jquerylib_0.1.4  
[41] farver_2.1.0      generics_0.1.1    jsonlite_1.7.2    vroom_1.5.7      
[45] magrittr_2.0.2    Matrix_1.2-18     ggbeeswarm_0.6.0  Rcpp_1.0.8       
[49] munsell_0.5.0     fansi_1.0.2       gdtools_0.1.9     lifecycle_1.0.1  
[53] stringi_1.7.6     whisker_0.3-2     yaml_2.2.1        plyr_1.8.6       
[57] grid_3.6.1        blob_1.2.2        ggrepel_0.9.1     parallel_3.6.1   
[61] promises_1.0.1    crayon_1.5.0      lattice_0.20-38   haven_2.4.3      
[65] hms_1.1.1         knitr_1.36        pillar_1.6.4      igraph_1.2.10    
[69] rjson_0.2.20      rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16 
[73] reprex_2.0.1      glue_1.6.2        evaluate_0.14     data.table_1.14.2
[77] modelr_0.1.8      vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[81] foreach_1.5.2     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[85] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[89] iterators_1.0.14  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2